官网网站开发,创建网站目录结构应遵循的方法,什么是网络推广,运维工程师累吗主要目标是适应摄像头rtsp流的检测
如果是普通文件夹或者图片#xff0c;run中的while True去掉即可。
web_client是根据需求创建的客户端#xff0c;将检测到的数据打包发送给服务器
# YOLOv5 #x1f680; by Ultralytics, GPL-3.0 licenseRun inf…主要目标是适应摄像头rtsp流的检测
如果是普通文件夹或者图片run中的while True去掉即可。
web_client是根据需求创建的客户端将检测到的数据打包发送给服务器
# YOLOv5 by Ultralytics, GPL-3.0 licenseRun inference on images, videos, directories, streams, etc.Usage:$ python path/to/detect.py --source path/to/img.jpg --weights yolov5s.pt --img 640
import argparse
import json
import os
import sys
import time
import moment
from pathlib import Pathimport cv2
import numpy as np
import torch
import torch.backends.cudnn as cudnnFILE Path(__file__).resolve()
ROOT FILE.parents[0] # YOLOv5 root directory
if str(ROOT) not in sys.path:sys.path.append(str(ROOT)) # add ROOT to PATH
ROOT Path(os.path.relpath(ROOT, Path.cwd())) # relativefrom models.experimental import attempt_load
from utils.datasets import LoadImages, LoadStreams
from utils.general import apply_classifier, check_img_size, check_imshow, check_requirements, check_suffix, colorstr, \increment_path, non_max_suppression, print_args, save_one_box, scale_coords, set_logging, \strip_optimizer, xyxy2xywh
from utils.plots import Annotator, colors
from utils.torch_utils import load_classifier, select_device, time_syncfrom mytools import read_yaml_all, base64_encode_img
from message_base import MessageBase
from websocket_client import WebClientclass Detect:def __init__(self, config: dict, client: WebClient):self.config configself.weights self.config.get(weights) # weights pathself.source self.config.get(source) # source self.imgsz self.config.get(imgsz) # imgszself.conf_thres self.config.get(conf_thres)self.iou_thres self.config.get(iou_thres)self.max_det self.config.get(max_det)self.device self.config.get(device) # cpu or 0,1,2,3self.view_img self.config.get(view_img) # show resultsself.save_txt self.config.get(save_txt) # save results to *.txtself.save_conf self.config.get(save_conf) # save confidences in --save-txt labelsself.save_crop self.config.get(save_crop) # save cropped prediction boxesself.nosave self.config.get(nosave) # do not save images/videosself.classes self.config.get(classes) # filter by class: --class 0, or --class 0 2 3self.agnostic_nms self.config.get(agnostic_nms) # class-agnostic NMSself.augment self.config.get(augment) # augmented inferenceself.visualize self.config.get(visualize) # visualize featuresself.update self.config.get(update) # update all modelsself.save_path self.config.get(save_path) # save results to project/nameself.line_thickness self.config.get(line_thickness) # bounding box thickness (pixels)self.hide_labels self.config.get(hide_labels) # hide labelsself.hide_conf self.config.get(hide_conf) # hide confidencesself.half self.config.get(half) # use FP16 half-precision inferenceself.dnn self.config.get(dnn) # use OpenCV DNN for ONNX inferenceself.func_device self.config.get(func_device) # 对应功能的设备名字self.save_img not self.nosave and not self.source.endswith(.txt) # save inference imagesself.webcam self.source.isnumeric() or self.source.endswith(.txt) or self.source.lower().startswith((rtsp://, rtmp://, http://, https://))set_logging()self.device select_device(self.device)self.half self.device.type ! cpu # half precision only supported on CUDAself.model attempt_load(self.weights, map_locationself.device)self.imgsz check_img_size(self.imgsz, sint(self.model.stride.max()))self.stride int(self.model.stride.max())self.names self.model.module.names if hasattr(self.model, module) else self.model.names# 获取数据if self.webcam:self.view_img check_imshow()cudnn.benchmark True # set True to speed up constant image size inferenceself.dataset LoadStreams(self.source, img_sizeself.imgsz, strideself.stride, autoTrue)self.bs len(self.dataset) # batch_sizeelse:self.dataset LoadImages(self.source, img_sizeself.imgsz, strideself.stride, autoTrue)self.bs 1 # batch_sizeself.client client # 客户端self.last_time moment.now()self.check_time_step 5 # 每隔多少时间检测一次os.mkdir(self.save_path) if not os.path.exists(self.save_path) else Nonedef inference(self, img):img torch.from_numpy(img).to(self.device)img img.half() if self.half else img.float() # uint8 to fp16/32img / 255.0 # 0 - 255 to 0.0 - 1.0if img.ndimension() 3:img img.unsqueeze(0)pred self.model(img, augmentself.augment)[0]# NMSpred non_max_suppression(pred, self.conf_thres, self.iou_thres,self.classes, self.agnostic_nms, max_detself.max_det)return preddef process(self, im0s, img, pred, path):for i, det in enumerate(pred): # per imageif self.webcam: # batch_size 1p, s, im0, frame path[i], f{i}: , im0s[i].copy(), self.dataset.countelse:p, s, im0, frame path, , im0s.copy(), getattr(self.dataset, frame, 0)p Path(p) # to Pathtxt_path str(self.save_path / labels / p.stem) ( if self.dataset.mode image else f_{frame}) # img.txts %gx%g % img.shape[2:] # print stringgn torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwhimc im0.copy() if self.save_crop else im0 # for save_cropannotator Annotator(im0, line_widthself.line_thickness, examplestr(self.names))if len(det):# Rescale boxes from img_size to im0 sizedet[:, :4] scale_coords(img.shape[2:], det[:, :4], im0.shape).round()# Print resultsfor c in det[:, -1].unique():n (det[:, -1] c).sum() # detections per classs f{n} {self.names[int(c)]}{s * (n 1)}, # add to string# Write resultsfor *xyxy, conf, cls in reversed(det):c int(cls)label self.names[c]# if label person:if label: # 根据对应标签做处理# annotator.box_label(xyxy, label, colorcolors(c, True)) # 画框t int(time.time())img_path f{self.save_path}/{self.func_device}_{label}_{t}.jpgcrop save_one_box(xyxy, imc, img_path, BGRTrue)x1, y1, x2, y2 int(xyxy[0]), int(xyxy[1]), int(xyxy[2]), int(xyxy[3])data {device: self.func_device,value: {label: label,time: t,locate: (x1, y1, x2, y2),crop: base64_encode_img(crop)}}data json.dumps(data) # 打包数据try:self.client.send(data) # 客户端发送数据passexcept Exception as err:print(发送失败:, err)self.client.connect()self.client.send(data)print(重连成功)print(data)# if self.save_txt: # Write to file# xywh (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(# -1).tolist() # normalized xywh# line (cls, *xywh, conf) if self.save_conf else (cls, *xywh) # label format# with open(txt_path .txt, a) as f:# f.write((%g * len(line)).rstrip() % line \n)# 画框# if self.save_img or self.save_crop or self.view_img: # Add bbox to image# c int(cls) # integer class# label None if self.hide_labels else (self.names[c] if self.hide_conf else# f{self.names[c]} {conf:.2f})# annotator.box_label(xyxy, label, colorcolors(c, True))def run(self):self.client.connect()while True:for path, img, im0s, vid_cap in self.dataset:if self.last_time.__lt__(moment.now()):self.last_time moment.now().add(secondsself.check_time_step)try:pred self.inference(img)self.process(im0s, img, pred, path) except Exception as err:print(err)if self.save_txt or self.save_img:s f\n{len(list(self.save_path.glob(labels/*.txt)))} labels saved to {self.save_path / labels} if self.save_txt else print(fResults saved to {colorstr(bold, self.save_path)}{s})if self.update:strip_optimizer(self.weights) # update model (to fix SourceChangeWarning)if __name__ __main__:message_base MessageBase()wc WebClient(192.168.6.28, 8000)configs read_yaml_all(yolo_configs.yaml)config read_yaml_all(configs.yaml)device_name config.get(DEVICE_LIST)[0]device_source config.get(RTSP_URLS).get(device_name)configs[source] device_sourceconfigs[func_device] device_nameprint(configs)detect Detect(configs, wc)detect.run()
